scholarly journals An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings

2018 ◽  
Author(s):  
Eleanor Sanderson ◽  
George Davey Smith ◽  
Frank Windmeijer ◽  
Jack Bowden
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Single Sample ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Level Data ◽  
Wide Range ◽  
Using Data ◽  
Summary Data

AbstractBackgroundMendelian Randomisation (MR) is a powerful tool in epidemiology which can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to Multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.Methods/ResultsWe use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK biobank to estimate the effect of education and cognitive ability on body mass index.ConclusionMVMR analysis consistently estimates the effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual or summary level data.

scholarly journals An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings

2018 ◽  
Vol 48 (3) ◽  
pp. 713-727 ◽  
Author(s):  
Eleanor Sanderson ◽  
George Davey Smith ◽  
Frank Windmeijer ◽  
Jack Bowden
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Single Sample ◽  
Uk Biobank ◽  
Level Data ◽  
Wide Range ◽  
Using Data ◽  
Summary Data

Abstract Background Mendelian randomization (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilizing genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome. Methods and results We use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single-sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK Biobank to estimate the effect of education and cognitive ability on body mass index. Conclusion MVMR analysis consistently estimates the direct causal effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual- or summary-level data.

scholarly journals Polynomial Mendelian Randomization reveals widespread non-linear causal effects in the UK Biobank

2021 ◽  
Author(s):  
Jonathan Sulc ◽  
Jenny Sjaarda ◽  
Zoltan Kutalik
Keyword(s):  
Causal Inference ◽  
Large Scale ◽  
Causal Effect ◽  
Causal Effects ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Individual Level ◽  
Glucose Levels ◽  
Causal Function ◽  
The Uk

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

scholarly journals Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

2020 ◽  
Author(s):  
Ciarrah Barry ◽  
Junxi Liu ◽  
Rebecca Richmond ◽  
Martin K Rutter ◽  
Deborah A Lawlor ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
General Procedure ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Individual Level ◽  
Level Data ◽  
Summary Data ◽  
Collider Bias

AbstractOver the last decade the availability of SNP-trait associations from genome-wide association studies data has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification.In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. A weighted sum of these estimates is then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes.Our approach is closely related to the work of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our paper serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.

scholarly journals Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Liza Darrous ◽  
Ninon Mounier ◽  
Zoltán Kutalik
Keyword(s):  
Complex Traits ◽  
Causal Effect ◽  
Hdl Cholesterol ◽  
Disease Diagnosis ◽  
Causal Effects ◽  
Mendelian Randomisation ◽  
Simultaneous Estimation ◽  
Summary Statistics ◽  
High Systolic Blood Pressure ◽  
Wide Range

AbstractMendelian Randomisation (MR) is an increasingly popular approach that estimates the causal effect of risk factors on complex human traits. While it has seen several extensions that relax its basic assumptions, most suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritabilities, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR outperforms several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results with other MR methods, LHC-MR unravels new mechanisms (how disease diagnosis might lead to improved lifestyle) and reveals new causal effects (e.g. HDL cholesterol being protective against high systolic blood pressure), hidden from standard MR methods due to a heritable confounder of opposite effect direction.

scholarly journals Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

PLoS Genetics ◽  
2021 ◽  
Vol 17 (8) ◽  
pp. e1009703
Author(s):  
Ciarrah Barry ◽  
Junxi Liu ◽  
Rebecca Richmond ◽  
Martin K. Rutter ◽  
Deborah A. Lawlor ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
General Procedure ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Individual Level ◽  
Level Data ◽  
Summary Data ◽  
Collider Bias

Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.

scholarly journals Searching for the causal effects of BMI in over 300 000 individuals, using Mendelian randomization

2017 ◽  
Author(s):  
Louise A C Millard ◽  
Neil M Davies ◽  
Kate Tilling ◽  
Tom R Gaunt ◽  
George Davey Smith
Keyword(s):  
Body Mass Index ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
P Value ◽  
Protective Effects ◽  
Independent Subset ◽  
Uk Biobank ◽  
False Discovery ◽  
Causal Risk Factor

ABSTRACTMendelian randomization (MR) has been used to estimate the causal effect of body mass index (BMI) on particular traits thought to be affected by BMI. However, BMI may also be a modifiable, causal risk factor for outcomes where there is no prior reason to suggest that a causal effect exists. We perform a MR phenome-wide association study (MR-pheWAS) to search for the causal effects of BMI in UK Biobank (n=334 968), using the PHESANT open-source phenome scan tool. Of the 20 461 tests performed, our MR-pheWAS identified 519 associations below a stringent P value threshold corresponding to a 5% estimated false discovery rate, including many previously identified causal effects. We also identified several novel effects, including protective effects of higher BMI on a set of psychosocial traits, identified initially in our preliminary MR-pheWAS and replicated in an independent subset of UK Biobank. Such associations need replicating in an independent sample.

scholarly journals Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics

2020 ◽  
Author(s):  
Liza Darrous ◽  
Ninon Mounier ◽  
Zoltán Kutalik
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Causal Effect ◽  
Causal Effects ◽  
Mendelian Randomisation ◽  
Simultaneous Estimation ◽  
Summary Statistics ◽  
Outcome Relationship ◽  
Wide Range ◽  
Complex Human Traits

AbstractMendelian Randomisation (MR), an increasingly popular method that estimates the causal effects of risk factors on complex human traits, has seen several extensions that relax its basic assumptions. However, most of these extensions suffer from two major limitations; their under-exploitation of genome-wide markers, and sensitivity to the presence of a heritable confounder of the exposure-outcome relationship. To overcome these limitations, we propose a Latent Heritable Confounder MR (LHC-MR) method applicable to association summary statistics, which estimates bi-directional causal effects, direct heritability, and confounder effects while accounting for sample overlap. We demonstrate that LHC-MR out-performs several existing MR methods in a wide range of simulation settings and apply it to summary statistics of 13 complex traits. Besides several concordant results, LHC-MR unravelled new mechanisms (how being diagnosed for certain diseases might lead to improved lifestyle) and revealed potential false positive findings of standard MR methods (apparent causal effect of body mass index on educational attainment may be driven by a strong ignored confounder). Phenome-wide search to identify LHC-implied heritable confounders showed remarkable agreement between the LHC-estimated causal effects of the latent confounder and those for the potentially identified ones. Finally, LHC-MR naturally decomposes genetic correlation to causal effect-driven and confounder-driven contributions, demonstrating that the genetic correlation between systolic blood pressure and diabetes is predominantly confounder-driven.

scholarly journals Causality of Abdominal Obesity on Cognition: a Trans-ethnic Mendelian Randomization study

2021 ◽  
Author(s):  
Shi-Heng Wang ◽  
Mei-Hsin Su ◽  
Chia-Yen Chen ◽  
Yen-Feng Lin ◽  
Yen-Chen Anne Feng ◽  
...  
Keyword(s):  
Cognitive Aging ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
European Ancestry ◽  
P Value ◽  
Abdominal Adiposity ◽  
Uk Biobank ◽  
Reverse Causality ◽  
Summary Data

Obesity has been associated with cognition in observational studies; however, whether its effect is confounding, reverse causality, or causal remains inconclusive. Using two-sample Mendelian randomization (MR) analyses, we investigated the causality of overall obesity, measured by BMI, and abdominal adiposity, measured by waist-hip ratio adjusted for BMI (WHRadjBMI), on cognition. Using summary data from the GIANT consortium, COGENT consortium, and UK Biobank of European ancestry, there was no causal effect of BMI on cognition performance (beta[95% CI]=-0.04[-0.12,0.04], p-value=0.35); however, a 1-SD increase in WHRadjBMI was associated with 0.07 standardized decrease in cognition performance (beta[95% CI]=-0.07[-0.12,-0.02], p=0.006). Using raw data from the Taiwan Biobank of Asian ancestry, there was no causal effect of BMI on cognitive aging (beta[95% CI]=0.00[-0.09,0.09], p-value=0.95); however, a 1-SD increase in WHRadjBMI was associated with a 0.17 standardized decrease in cognitive aging (beta[95% CI]=-0.17[-0.30,-0.03], p=0.02). This trans-ethnic MR study reveals that abdominal adiposity impairs cognition.

scholarly journals Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption

2017 ◽  
Author(s):  
Jack Bowden ◽  
Fabiola Del Greco M ◽  
Cosetta Minelli ◽  
Qingyuan Zhao ◽  
Debbie A Lawlor ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Causal Effect ◽  
Meta Analysis ◽  
Type I ◽  
Weak Instruments ◽  
Using Data ◽  
Summary Data

AbstractBackgroundTwo-sample summary data Mendelian randomization (MR) incorporating multiple genetic variants within a meta-analysis framework is a popular technique for assessing causality in epidemiology. If all genetic variants satisfy the instrumental variable (IV) and necessary modelling assumptions, then their individual ratio estimates of causal effect should be homogeneous. Observed heterogeneity signals that one or more of these assumptions could have been violated.MethodsCausal estimation and heterogeneity assessment in MR requires an approximation for the variance, or equivalently the inverse-variance weight, of each ratio estimate. We show that the most popular ‘1st order’ weights can lead to an inflation in the chances of detecting heterogeneity when in fact it is not present. Conversely, ostensibly more accurate ‘2nd order’ weights can dramatically increase the chances of failing to detect heterogeneity, when it is truly present. We derive modified weights to mitigate both of these adverse effects.ResultsUsing Monte Carlo simulations, we show that the modified weights outperform 1st and 2nd order weights in terms of heterogeneity quantification. Modified weights are also shown to remove the phenomenon of regression dilution bias in MR estimates obtained from weak instruments, unlike those obtained using 1st and 2nd order weights. However, with small numbers of weak instruments, this comes at the cost of a reduction in estimate precision and power to detect a causal effect compared to 1st order weighting. Moreover, 1st order weights always furnish unbiased estimates and preserve the type I error rate under the causal null. We illustrate the utility of the new method using data from a recent two-sample summary data MR analysis to assess the causal role of systolic blood pressure on coronary heart disease risk.ConclusionsWe propose the use of modified weights within two-sample summary data MR studies for accurately quantifying heterogeneity and detecting outliers in the presence of weak instruments. Modified weights also have an important role to play in terms of causal estimation (in tandem with 1st order weights) but further research is required to understand their strengths and weaknesses in specific settings.

scholarly journals Polynomial Mendelian Radomization reveals widespread non-linear causal effects in the UK Biobank

2022 ◽  
Author(s):  
Jonathan Sulc ◽  
Jennifer Sjaarda ◽  
Zoltan Kutalik
Keyword(s):  
Causal Inference ◽  
Large Scale ◽  
Causal Effect ◽  
Causal Effects ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Individual Level ◽  
Glucose Levels ◽  
Causal Function ◽  
The Uk

Abstract Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

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